Sensory Evaluation Prediction System, Suspension Device, and Suspension Control System

ABSTRACT

A sensory evaluation prediction system includes an input unit that reads an output from a behavior sensor that measures two or more types of pieces of time series information regarding a moving body, a selection unit that selects two or more types of physical quantities from the output from the behavior sensor read by the input unit, a correlation creation unit that creates information showing a correlation in time series between the two or more types of the physical quantities selected by the selection unit, and an evaluation circuit that calculates an evaluation value of a sensory index based on the information showing the correlation in time series.

TECHNICAL FIELD

The present invention relates to a sensory evaluation prediction system,a suspension device, and a suspension control system.

BACKGROUND ART

In an automobile, a vibration stimulus input from a road surface istransmitted to a vehicle occupant via a tire, a suspension, a chassis, aseat rail, a seat leg, and a sheet material. It is mainly important inride quality sensory evaluation that how a driver and the vehicleoccupant feels about this vibration stimulus. Additionally, in steeringstability sensory evaluation, a reaction when a steering is operated, acomfort of response, and presence/absence of uncomfortable feeling aremainly important. In an automobile manufacturer, there is a ride qualitytargeted by each of automobile manufacturers, steering stability, and abalance between them, and the ride quality and the steering stabilityhave been improved by conveying improvements from trained expert driversto persons in charge of design of vehicle components and adjustment ofparameters. Patent Literature 1 discloses a motion evaluation methodthat detects at least a jerk found by differentiating an acceleration ofan object in motion, inputs the detected jerk to an input layer in ahierarchical neural network, and outputs a motion evaluation result froman output layer via a middle layer.

CITATION LIST Patent Literature

Patent Literature 1: Japanese Unexamined Patent Application PublicationNo. Hei 7(1995)-244065

SUMMARY OF INVENTION Technical Problem

The invention described in Patent Literature 1 has room for improvementin handling of a variation in an operation depending on a driver.

Solution to Problem

A sensory evaluation prediction system according to a first aspect ofthe present invention includes an input unit, a selection unit, acorrelation creation unit, and an evaluation circuit. The input unitreads an output from a behavior sensor that measures two or more typesof pieces of time series information regarding a moving body. Theselection unit selects two or more types of physical quantities from theoutput from the behavior sensor read by the input unit. The correlationcreation unit creates information showing a correlation in time seriesbetween the two or more types of the physical quantities selected by theselection unit. The evaluation circuit calculates an evaluation value ofa sensory index based on the information showing the correlation in timeseries.

A suspension device according to a second aspect of the presentinvention is manufactured based on the evaluation value output from theabove-described sensory evaluation prediction system.

A suspension control system according to a third aspect of the presentinvention includes the above-described sensory evaluation predictionsystem and a suspension damping force variable mechanism that adjusts adamping force of a suspension device mounted on the moving body based onthe evaluation value output from the sensory evaluation predictionsystem.

Advantageous Effects of Invention

According to the present invention, since the correlation between aplurality of the physical quantities is evaluated, a variation in anoperation depending on a driver is less likely to be affected.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a block diagram of a sensory evaluation prediction system in afirst embodiment.

FIG. 2 is a drawing illustrating an exemplary sensory index setting.

FIG. 3 is a drawing illustrating an exemplary data specificationsetting.

FIG. 4 is a drawing illustrating an example of data acquired by a sensorgroup installed in a vehicle.

FIG. 5 is a conceptual diagram illustrating creation of correlationinformation showing a correlation between two physical quantities.

FIG. 6A is a diagram illustrating a correlation between a steeringtorque and a steering angle created by the method illustrated in FIG. 5. FIG. 6B is a diagram illustrating a state in which the informationillustrated in FIG. 6A is divided vertically and horizontally at aresolution of 6 bits each.

FIG. 7 is a conceptual diagram illustrating an operation of anevaluation circuit employing a hierarchical neural network.

FIG. 8 is a drawing illustrating a relationship between outputs from thehierarchical neural network illustrated in FIG. 7 and sensory indexvalues.

FIG. 9 is a diagram illustrating an example in which the correlationinformation is created using three types of physical quantities.

FIG. 10A and FIG. 10B include drawings illustrating an example of theevaluation circuit for each sensory index. FIG. 10(a) is a diagramillustrating a relationship between an input layer, a hidden layer, andan output layer in the hierarchical neural network. FIG. 10(b) is adrawing illustrating an exemplary relationship between the sensoryindex, two physical quantities for evaluation, the number of hiddenlayer elements, and the number of output layer elements.

FIG. 11 is a flowchart depicting a flow of processes of the sensoryevaluation prediction system according to the first embodiment of theinvention.

FIG. 12 is a drawing illustrating exemplary steering operation andoutput waveforms of the evaluation circuits of the evaluation circuitsconstituting the sensory evaluation prediction system in the firstembodiment.

FIG. 13A and FIG. 13B include drawings illustrating examples ofvisualizing index values output by an aggregation unit constituting thesensory evaluation prediction system in the first embodiment.

FIG. 14 is a timing chart illustrating an exemplary relationship betweenan operation of the evaluation circuit and a traveling road surface.

FIG. 15 is a block diagram of a sensory evaluation prediction system ina second embodiment.

FIG. 16 is a flowchart depicting a flow of processes of the sensoryevaluation prediction system according to the second embodiment.

FIG. 17 is a block diagram of a sensory evaluation prediction system ina third embodiment.

FIG. 18 is a flowchart depicting processes of a learning function of thesensory evaluation prediction system in the third embodiment.

FIG. 19 is a block diagram illustrating a functional configuration of asensory evaluation prediction system according to a fourth embodiment.

FIG. 20 is a block diagram illustrating a functional configuration of asuspension control system according to a fifth embodiment.

DESCRIPTION OF EMBODIMENTS First Embodiment

The following will describe a first embodiment of a sensory evaluationprediction system with reference to FIG. 1 to FIG. 14 . The sensoryevaluation prediction system described below may be mounted on a vehicleor may be installed outside a vehicle.

FIG. 1 is a block diagram of a sensory evaluation prediction system 101in the first embodiment. The sensory evaluation prediction system 101includes a test result storage unit 102, a control unit 103, a register104, a selection unit 105, an evaluation index determination unit 106,an evaluation unit 107, an aggregation unit 108, a weight parameterstorage unit 109, an aggregation result storage unit 110, a display unit111, and an input unit 115. The register 104 includes a sensory indexsetting 112, a data specification setting 113, and an aggregation modesetting 114. The evaluation unit 107 includes a first correlationcreation unit 121, a second correlation creation unit 122, a thirdcorrelation creation unit 123, a fourth correlation creation unit 124, afifth correlation creation unit 125, a first evaluation circuit 131, asecond evaluation circuit 132, a third evaluation circuit 133, a fourthevaluation circuit 134, and a fifth evaluation circuit 135.

The first correlation creation unit 121 creates first correlationinformation and inputs it to the first evaluation circuit 131. Thesecond correlation creation unit 122 creates second correlationinformation and inputs it to the second evaluation circuit 132. Thethird correlation creation unit 123 creates third correlationinformation and inputs it to the third evaluation circuit 133. Thefourth correlation creation unit 124 creates fourth correlationinformation and inputs it to the fourth evaluation circuit 134. Thefifth correlation creation unit 125 creates fifth correlationinformation and inputs it to the fifth evaluation circuit 135.

Hereinafter, the first correlation creation unit 121, the secondcorrelation creation unit 122, the third correlation creation unit 123,the fourth correlation creation unit 124, and the fifth correlationcreation unit 125 will be collectively referred to as a correlationcreation unit 120. Hereinafter, the first evaluation circuit 131, thesecond evaluation circuit 132, the third evaluation circuit 133, thefourth evaluation circuit 134, and the fifth evaluation circuit 135 willbe collectively referred to as an evaluation circuit 130. Hereinafter,the first correlation information, the second correlation information,the third correlation information, the fourth correlation information,and the fifth correlation information will be collectively referred toas correlation information.

Respective sensory indices evaluated by the five evaluation circuits 130are a neutral (N) response, a yaw response, a grip feeling, a rollfeeling, and a straightness. However, these are merely an example, andthe evaluation circuit 130 may evaluate a sensory index other than theabove-described sensory indices. The number of sensory indices evaluatedby the evaluation circuit 130 only needs to be two or more, and thenumber does not have an upper limit. FIG. 1 illustrates an example inwhich the evaluation unit 107 evaluates the five sensory indices, andaccording to the number of sensory indices to be evaluated, the numbersof the correlation creation units 120 and the evaluation circuits 130increase and decrease.

The control unit 103, the selection unit 105, the evaluation indexdetermination unit 106, the evaluation unit 107, and the aggregationunit 108 perform calculations. These calculations are achieved by, forexample, reading a program from a ROM (not illustrated), expanding it toa RAM (not illustrated), and executing it by a CPU (not illustrated).However, these calculations may be achieved by a Field Programmable GateArray (FPGA) as a rewritable logic circuit (JIS) and an ApplicationSpecific Integrated Circuit (ASIC) as an integrated circuit for aspecific application. Instead of the combination of the CPU, the ROM,and the RAM, these calculations may be achieved by a combination ofdifferent configurations, for example, a combination of a CPU, a ROM, aRAM, and an FPGA.

The test result storage unit 102, the register 104, the weight parameterstorage unit 109, and the aggregation result storage unit 110 arenon-volatile storage devices, and each of them can be referred to as a“storage unit.” However, at least one of the test result storage unit102, the register 104, the weight parameter storage unit 109, and theaggregation result storage unit 110 may be a volatile storage device,and in this case, information read from a non-volatile storage device(not illustrated) at the start of the sensory evaluation predictionsystem 101 is stored in the volatile storage device. The display unit111 is, for example, a liquid crystal display, and displays a videosignal output from the control unit 103. The input unit 115 is aconnection interface with the test result storage unit 102.

The test result storage unit 102 stores a learning target 1021 and anevaluation target 1022. The learning target 1021 is a combination ofsensor data acquired by a sensor group mounted on an evaluated vehiclein which an expert driver gets and the sensory indices by the expertdriver at the time. The sensor group will be described later withreference to FIG. 4 . The learning target 1021 is used to calculate aweight parameter stored in the weight parameter storage unit 109 asdescribed later. Note that the evaluation unit 107 refers to the weightparameter storage unit 109. The evaluation target 1022 is sensor dataacquired by the sensor group mounted on the evaluated vehicle. Theevaluation target 1022 is evaluated by the evaluation unit 107.

The control unit 103 has a function that causes respective blocksconstituting the sensory evaluation prediction system 101 tocooperatively operate. That is, although the control unit 103 involvesall processes described later, for simplification of description, theinvolvement of the control unit 103 in the process will not beespecially described below. Note that the control unit 103 has afunction that stops the evaluation circuit 130 that need not operatebased on the output from the evaluation index determination unit 106.For example, when the evaluation index determination unit 106 selectsonly the N response, the second evaluation circuit 132 to the fifthevaluation circuit 135 are stopped.

The sensory index setting 112 included in the register 104 is a registerin which the sensory index displayed in the display unit 111 is set. Anygiven value is preliminarily set to the sensory index setting 112.However, the sensory index setting 112 may be settable from outside thesensory evaluation prediction system 101.

FIG. 2 is a drawing illustrating the exemplary sensory index setting112. Here, the sensory index setting 112 is constituted of m bits, forexample, 5 bits, and the sensory index is assigned to each bit. Theevaluation index determination unit 106 sets “1” to the bitcorresponding to the sensory index as the evaluation target, and sets“0” to bits other than the bit. Note that FIG. 2 is merely an example,and as long as the similar setting is possible, any data format may beused for the sensory index setting 112. Returning to FIG. 1 , thedescription will be continued.

The data specification setting 113 included in the register 104 is aregister in which data used for the respective sensory indices, that is,specifications of combinations of sensor outputs are set. In thisembodiment, the data specification setting 113 is not changed.

FIG. 3 is a drawing illustrating the exemplary data specificationsetting 113. The data specification setting 113 is constituted of tablesby the same number of the sensory indices, for example, m tables, andn-bit information is stored in each table. Sensor information stored inthe test result storage unit 102, namely, a physical quantity isassigned to each bit in each table. That is, “1” is set to the bitcorresponding to the physical quantity used to calculate thecorresponding sensory index, and “0” is set to bits other than the bit.Note that FIG. 3 is merely an example, and as long as the similarsetting is possible, any data format is used for the data specificationsetting 113. Returning to FIG. 1 , the description will be continued.

The aggregation mode setting 114 included in the register 104 is settinginformation that indicates which of an instantaneous value and anaverage value is output from the evaluation unit 107 and to be displayedin the display unit 111. The aggregation unit 108 reads the aggregationmode setting 114. The selection unit 105 outputs at least a part of theevaluation target 1022 read from the test result storage unit 102 by theinput unit 115 to the respective correlation creation units 120. Theselection unit 105 refers to the output from the evaluation indexdetermination unit 106 received via the control unit 103 and the dataspecification setting 113 to determine which of the data included in theevaluation target 1022 is output.

The evaluation index determination unit 106 selects the sensory index toperform sensory evaluation prediction based on an operating situation ofsteering. One idea is that the operating situation of steering isdefined based on ISO 13674-1/2 (Road vehicles—Test method for thequantification of on-centre handling Part 1/2) regulating a testingmethod of a test run for steering stability. For example, when theevaluation index determination unit 106 determines that a Weave testincluding continuous S-shaped curves, what is called a slalom run, isperformed based on the operating situation of steering, the evaluationindex determination unit 106 determines the N response, the yawresponse, the grip feeling, and the roll feeling as the evaluationtarget, and excludes the straightness from the evaluation target. Forexample, when the evaluation index determination unit 106 determinesthat straight traveling is performed, the evaluation index determinationunit 106 determines the N response and the straightness as theevaluation target, and excludes the yaw response, the grip feeling, andthe roll feeling from the evaluation target. The operating situation ofsteering is, for example, any of a steering position, a steering speedas differentiation of the steering position, and a steering accelerationas differentiation of the steering speed. Hereinafter, informationindicative of the operating situation of steering is referred to assteering information in some cases.

An operation pattern of steering assumed by the evaluation indexdetermination unit 106 includes Step steer as a pattern of a stepwisesteering operation in which straight traveling is performed for acertain period and then a regulated steering angle is maintained, andthe sensory index corresponding to this operation pattern ispreliminarily determined. When the steering operation is reworded withthe sensory index as a reference, a steering operation assumed for eachsensory index is present. Accordingly, the steering information referredto by the evaluation index determination unit 106 is the operatingsituation of steering during traveling, and the sensory evaluationprediction system 101 determines the sensory index for sensoryevaluation prediction based on this. Note that the determination of thesensory index only needs to group the above-described defined operatingsituations of steering including a difference in parameter, such as avehicle speed, and to ensure determination, and, for example, patternmatching of steering angle data of steering can be used.

The evaluation unit 107 includes the correlation creation unit 120 andthe evaluation circuit 130. Using two or more types of physicalquantities transferred from the selection unit 105, the correlationcreation unit 120 creates correlation information as information showinga correlation in time series between the physical quantities. In thecreation method of the correlation information by each of thecorrelation creation units 120, for example, the types of physicalquantities used for creation, scaling setting, and an order of data usedmay be the same or may be different.

The aggregation unit 108 aggregates the sensory index values output bythe evaluation circuit 130. As described above, since the steeringoperation as the evaluation target is present for each of the sensoryindices, when traveling is performed by a steering operation out of thetarget, the evaluation circuit 130 possibly fails to output theappropriate sensory index value. Therefore, based on the determinationresult by the evaluation index determination unit 106, the process isperformed on the sensory index value output by the evaluation circuit130 only when the traveling is performed by the steering operation asthe evaluation target. When the traveling is performed by the steeringoperation out of the evaluation target, the sensory index value outputby the evaluation circuit 130 is eliminated. The aggregation unit 108determines the necessity for elimination of the calculation result foreach sensory index and writes the sensory index value that has not beeneliminated to the aggregation result storage unit 110 together with atime stamp.

The weight parameter storage unit 109 stores the parameter used by theevaluation circuit 130. Since the five types of sensory indices areassumed in this embodiment, the weight parameter storage unit 109 has acapacity of ensuring storing at least five sets of parameters. Note thatthe parameter here is, for example, a coefficient of a calculationformula used by the evaluation circuit 130 and a weight Wij ofconnection between elements, for example, when the evaluation circuit130 is achieved by a hierarchical neural network.

The display unit 111 presents the sensory index value stored in theaggregation result storage unit 110 to the vehicle occupant in thevehicle. Note that the sensory index output from the display unit 111 isselectable, and is selectable from outside with the sensory indexsetting 112 included in the register 104. Additionally, as the sensoryindex value output by the display unit 111, any of the instantaneousvalue or the average value in a traveling period is selectable, and thesensory index value can be set from outside with the aggregation modesetting 114 included in the register 104.

FIG. 4 is a drawing illustrating an example of data acquired by thesensor group installed in a vehicle 201. The respective sensorsillustrated in FIG. 4 measure behaviors of own vehicle and therefore canbe referred to as “behavior sensors.” A pitch rate, a roll rate, and ayaw rate indicated by a reference numeral 202 are an example of dataacquired in vehicle behavior used for sensory evaluation prediction. Avertical acceleration, a longitudinal acceleration, and a lateralacceleration indicated by a reference numeral 203 are an example of dataacquired by a chassis section used for sensory evaluation prediction. Avehicle speed, a steering, a GPS, a camera, and a radar indicated by areference numeral 204 are example of data acquired by another member forsensory evaluation prediction.

Especially, a stimulus from a road surface is input to the chassissection via a tire, and all or a part of acceleration data regarding aroute reaching the vehicle occupant is selected. Examples include aspring lower portion and a spring upper portion constituting asuspension, a component around a seat on which the vehicle occupant isseated, a tie rod of steering, and a steering. Further, although notillustrated in FIG. 4 , information of an accelerator, a brake, or thelike as an operation target of the driver may be able to be acquired.Note that when the sensory evaluation prediction for steering stabilityis achievable, a measurement point other than the parts listed here maybe employed. Although the information acquired by the sensor groupdescribed in FIG. 4 is stored in the test result storage unit 102, thesensory evaluation prediction system 101 is not necessarily stored inthe vehicle 201.

With reference to FIG. 5 to FIG. 8 , an outline of a process thatestimates the sensory index using two physical quantities will bedescribed. Thereafter, with reference to FIG. 9 , an outline of aprocess that estimates the sensory index using three physical quantitieswill be described. As described above, the correlation information showsthe correlation in time series of the two or more physical quantity, andthere may be a case where a correlation in time series of three or fouror more physical quantities are shown. However, in FIG. 5 to FIG. 8 , anexample in which the sensory index is estimated using a correlation oftwo physical quantities, namely, a first physical quantity P1 and asecond physical quantity P2, which is the simplest case, will bedescribed.

FIG. 5 is a conceptual diagram illustrating creation of the correlationinformation showing the correlation between the two physical quantities.The upper portion in FIG. 5 is a time series drawing indicating thephysical quantity P1 by the solid line and indicating the physicalquantity P2 by the dashed line, and as it goes to the right side in thedrawing, the time passes. Changes in the physical quantity P1 and thephysical quantity P2 start at a time t0, and the time passes to a timet1, a time t2, a time t3, and a time t4. The lower portion of FIG. 5shows the correlation relationship between the physical quantity P1 andthe physical quantity P2 from the time t0 until each of the times fromthe times t1 to t4. The lower portion in FIG. 5 plots the value ofphysical quantity P1 on the horizontal axis and the value of thephysical quantity P2 on the vertical axis.

For example, at the time t1, the plot is made in a first quadrant of aplot diagram, at the time t2, the plot is made toward a second quadrantof the plot diagram, at the time t3, the plot is made in a thirdquadrant, and at the time t4, the plot is made in a fourth quadrant.Continuation of them creates a scatter diagram that visualizes thecorrelation relationship between the physical quantity P1 and thephysical quantity P2.

FIG. 6(a) is a diagram illustrating a correlation between a steeringtorque and a steering angle created by the method illustrated in FIG. 5. FIG. 6(b) is a diagram illustrating a state in which the informationillustrated in FIG. 6(a) is divided vertically and horizontally at aresolution of 6 bits each.

FIG. 6(b) will be described in detail. The steering torque indicated onthe horizontal axis probably takes values on the positive side and thenegative side around 0 [N·m]. Additionally, when the steering angleindicated on the vertical axis in the neutral steering state in whichstraight traveling is performed is set to 0 [deg], for example, thesteering angle is expressed so as to have a positive value at steeringto the right and have a negative value at steering to the left. When therespective 0 values are assigned to 31 (decadal system) in a digital6-bit space, a scatter diagram in which the positive side and thenegative side are balanced is created.

Note that the correlation creation unit 120 has a function that derivesmaximum values and minimum values of the physical quantities stored inthe test result storage unit 102, for example, the steering angle andthe steering torque assumed in a steering operation under a designatedtravel condition. The designated travel condition here means acondition, such as “slalom run at 0.2 Hz and the maximum lateralacceleration of 0.4 G under the condition of speed per hour of 100 kmh.”Additionally, physical quantity data under the condition are acquiredfrom the test result storage unit 102 to derive the maximum value andthe minimum value by comparative calculation. The value of the largerabsolute value among the maximum value and the minimum value is used tobe normalized. Furthermore, when digitization in which “1” is set withplot and “0” is set without plot is performed, an image that allowsconfirmation of the whole trend while the 0 value is set to 31 (thedecadal system) can be created.

More specifically, a raster image of 64 pixels in vertical and 64 pixelsin horizontal and information in which the respective pixels express thepresence/absence of plot by 1 and 0 is the correlation information. Thiscorrelation information is, for example, expressed as a 4096-dimensionalcolumn vector.

FIG. 7 is a conceptual diagram illustrating the operation of theevaluation circuit 130 employing the hierarchical neural network. InFIG. 7 , an evaluation circuit 401 equivalent to each of the evaluationcircuits 130 illustrated in FIG. 1 is constituted by the hierarchicalneural network having a three-layer configuration in which respectiveelements of an input layer (the number of elements I+1), a hidden layer(the number of elements J+1), and an output layer (the number ofelements K) are hierarchically coupled. Note that as illustrated in FIG.7 to each of the input layer and the hidden layer, one elementrepresenting a bias term is set. Each of the elements of the input layeris coupled to each of the elements of the hidden layer with weight W1ij(i=1 to I+1, j=1 to J+1), and each of the elements of the hidden layeris coupled to each of the elements of the output layer by weight W2jk(j=1 to J+1, k=1 to K). As described above, the weight parameter storageunit 109 stores the information of the weights.

As described in FIG. 6(b), when each of the physical quantities isexpressed by 6 bits, the scatter diagram can be considered as a digitalimage of 64 pixels×64 pixels expressing the presence/absence of plotwith the pixel values of 1 and 0. The information of the digital imageis input to the evaluation circuit 401. Assuming that data conversion isperformed with the 0 values of the respective physical quantities at thecenter of the digital space, the position of the pixel is meaningful,and therefore the pixel data itself is set to the input of thehierarchical neural network. As one example, inputs a11 toa1I of thehierarchical neural network are set in a dot-sequential manner from thepixel value at the upper left of the digital image to the lower right ofthe digital image. Assuming that all is the I-th input element, I=4096(=64×64) is met. In this neural network, only any one of the elements ofthe output layer outputs “1” and the output layer elements other thanthat outputs “0.”

FIG. 8 is a drawing illustrating a relationship between outputs from thehierarchical neural network illustrated in FIG. 7 and sensory indexvalues. The specifications of the output layer element are: the highestpoint of 8.00, the lowest point of 4.00, and the increment of the scoreof 0.25. The number of output layer elements K in this case is 17.

Determination of the weight parameter stored in the weight parameterstorage unit 109, what is called learning of the evaluation circuit 401is performed as follows. The learning target 1021 stored in the testresult storage unit 102 includes a large number of combinations ofsensor outputs while the expert driver gets in and the sensory indexvalues answered by the expert driver. In a certain test run, when theexpert driver answers the sensory index value of the N response as 7.75points, learning is performed using the input value and the output valueas the next combination. That is, for example, the input value is rasterimage information illustrated in FIG. 6(b) that plots the correlation intime series between the steering torque and the steering angle on thetwo-dimensional plane. Additionally, only the output value of an outputlayer element a32 corresponding to the 7.75 points is “1” and the outputvalues of the output layer elements other than that is “0.”

By using a large number of the combinations of the input values and theoutput values, the correlations between the large amount of time-seriesdata and the sensory index values answered by the expert driver arelearnt by hierarchical neural network. The generally known errorbackpropagation method (backpropagation) can be used as the learningmethod for the neural network.

FIG. 9 is a diagram illustrating an example in which the correlationinformation is created by selecting the three types of physicalquantities. The example illustrated in FIG. 9 shows an example ofcombining the physical quantities P1 to P3, the physical quantity P1 isset to the X-axis, a physical quantity P3 is set to the Y-axis, and thephysical quantity P2 is set to the Z-axis. The example illustrated inFIG. 6 is the correlation between the two physical quantities, andtherefore the physical quantities are plotted on the two-dimensionalplane. However, since FIG. 9 is the correlation between the threephysical quantities, the plot is made on a three-dimensional space.

Then, the three-dimensional space is divided into voxels havingpredetermined sizes, and any of the values of “1” and “0” is setdepending on the presence/absence of plot in the voxel. Further, thevalues of voxels output in the predetermined order are the correlationinformation. Since the process after the input of the createdcorrelation information to the evaluation circuit 130 is as described inFIG. 8 , the description will be omitted. Note that the four types ormore of the physical quantities are difficult to be visualized, andtherefore the description will be omitted here, but can be handled bythe similar method and the number of types has no upper limit. Forexample, a correlation in time series between 10 types of physicalquantities may be used as the correlation information.

FIG. 10 includes drawings illustrating an example of the evaluationcircuit for each sensory index. FIG. 10(a) is a diagram illustrating therelationship between an input layer, a hidden layer, and an output layerin the hierarchical neural network. FIG. 10(b) is a drawing illustratingan exemplary relationship between the sensory index, the two physicalquantities for evaluation, the number of hidden layer elements, and thenumber of output layer elements. The evaluation circuit illustrated inFIG. 10(a) is, similarly to the evaluation circuit 401 in FIG. 7 ,constituted by the hierarchical neural network in which an input layer501, a hidden layer 502, and an output layer 503 are coupledhierarchically. Note that in the sensory evaluation prediction system101 of this embodiment, like the first evaluation circuit 131 to thefifth evaluation circuit 135 in FIG. 1 , the evaluation circuit is setfor each of the sensory indices. As described above, the specificationsof the respective evaluation circuits may be the same or may bedifferent.

FIG. 10(b) is a drawing illustrating an example of the specificationsfor each of the evaluation circuits. For example, the evaluation circuitcorresponding to the N response, namely, the first evaluation circuit131 in FIG. 1 uses the steering torque and the steering angle as thephysical quantities for evaluation, and the number of hidden layerelements J=100. The evaluation circuit corresponding to the yawresponse, namely, the second evaluation circuit 132 in FIG. 1 uses thesteering angle and the yaw rate as the physical quantities forevaluation, and the number of hidden layer elements J=200. Additionally,the evaluation circuit corresponding to the grip feeling, namely, thethird evaluation circuit 133 in FIG. 1 uses the yaw rate and the lateralacceleration as the physical quantities for evaluation, and the numberof hidden layer elements J=250. The evaluation circuit corresponding tothe roll feeling, namely, th fourth evaluation circuit 134 in FIG. 1uses the lateral jerk as the physical quantity for evaluation and thenumber of hidden layer elements J=500. In all of the evaluationcircuits, the number of output layer elements is K=17. However, theselection of the physical quantities and the parameters are one example,and other parameters may be used.

FIG. 11 is a flowchart depicting a flow of processes of the sensoryevaluation prediction system according to the first embodiment of thepresent invention. First, at Step S701, the control unit 103 sets “0,”which represents that the sensory evaluation is not performed, to a flagvalue indicating an operating situation of sensory evaluation.

At Step S702, the control unit 103 determines whether sensory evaluationON has been set by, for example, the operation by the vehicle occupantin own vehicle, that is, whether an operation command of sensoryevaluation has been performed. In the case of the sensory evaluation ON,it is determined that the operation command of sensory evaluation hasbeen performed and the process proceeds to Step S703. In the case of thesensory evaluation OFF, it is determined that the operation command ofsensory evaluation has not been performed and the process proceeds toStep S715.

At Step S703, the control unit 103 sets “1,” which represents the startof operation, to the above-described flag value, which indicates theoperation situation of sensory evaluation. Next, the control unit 103acquires steering operation information (Step S704), and analyzes thesteering operation in time series (Step S705). At Step S706, the controlunit 103 determines the sensory index corresponding to the steeringoperation using the evaluation index determination unit 106.

At Step S707, the control unit 103 selects the evaluation circuitcorresponding to the sensory index determined as the evaluation index atStep S706 among the evaluation circuits 130 disposed for each of thesensory indices. At Step S708, the control unit 103 selects a storagearea in the aggregation result storage unit 110 corresponding to theevaluation circuit selected at Step S707 as a storage block forevaluation value.

At Step S709, the selection unit 105 extracts the evaluation target 1022in the test result storage unit 102 for a predetermined time range andcreates time-series data used as data for evaluation. At Step S710, theselection unit 105 adjusts an operation start timing of the evaluationcircuit 130 using nearby road surface information, vehicle speedinformation, and the like. Then, according to the timing afteradjustment, the time-series data created at Step S709 is expanded as thedata for evaluation to the evaluation circuit selected at Step S707.

At Step S711, the evaluation circuit selected at Step S707 among theevaluation circuits 130 calculates an evaluation value for theevaluation index determined at Step S706 based on the data forevaluation input from the selection unit 105 at Step S710.

At Step S712, the aggregation unit 108 determines whether a setaggregation mode is any of an instantaneous value aggregation mode andan average value aggregation mode based on the value of the aggregationmode setting 114. For example, when the value of the aggregation modesetting 114 is “0,” the aggregation unit 108 determines that theinstantaneous value aggregation mode is set and advances the process toStep S713. When the value the aggregation mode setting 114 is “1,” theaggregation unit 108 determines that the average value aggregation modeis set and advances the process to Step S714.

At Step S713, the aggregation unit 108 transfers the evaluation valuecalculated at Step S711 to the display unit 111 to cause the displayunit 111 to display it. Thus, the instantaneous value of the evaluationvalue for the evaluation index determined at Step S706 is output tooutside using the display unit 111. Note that there may be a case wherethe change in the instantaneous value is too fast and therefore isdifficult to be observed depending on the calculation cycle of theevaluation value. In the case, the average value may be calculated for apredetermined time and may be displayed instead of the instantaneousvalue.

At Step S714, the aggregation unit 108 writes the evaluation valuecalculated at Step S711 to the storage block selected at Step S708. Whenthe process at Step S713 or Step S714 ends, the process returns to StepS702, and the above-described process is repeated. Thus, until it isdetermined that the sensory evaluation OFF is set at Step S702, asequence of the processes from Step S703 to S714 is continuouslyperformed.

At Step S702, when it is determined that the sensory evaluation OFF isset, at Step S715, the control unit 103 determines whether “1” is set tothe above-described flag value indicating the operation situation ofsensory evaluation. When “1” is set to the flag value, it is determinedthat the sensory evaluation has been already operated in the sequence ofprocesses from Step S703 to S714 and the process proceeds to Step S716.When “0” is set to the flag value, it is determined that the sensoryevaluation has not been operated and the process returns to Step S701.

At Step S716, similarly to Step S712 described above, the aggregationunit 108 determines whether the set aggregation mode is any of theinstantaneous value aggregation mode and the average value aggregationmode. When the average value aggregation mode is set, the processproceeds to Step S717, and when the instantaneous value aggregation modeis set, the process proceeds to Step S701. At Step S717, the aggregationunit 108 reads the evaluation value stored in the aggregation resultstorage unit 110.

At Step S718, the aggregation unit 108 calculates the average value ofthe evaluation value for each evaluation index after starting theprocess in FIG. 11 based on the evaluation value read at Step S714. Atthe subsequent Step S719, the aggregation unit 108 transfers the averagevalue calculated at Step S718 to the display unit 111 and causes thedisplay unit 111 to display it. Accordingly, the average value of theevaluation value when own vehicle travels the road surface as theevaluation target is aggregated and is output to outside using thedisplay unit 111. After the process at Step S719 ends, the processreturns to Step S701.

FIG. 12 is a drawing illustrating exemplary steering operation andoutput waveforms of the evaluation circuits of the evaluation circuitsconstituting the sensory evaluation prediction system in the firstembodiment. Reference numeral 801 indicates the time series change insteering operation, and respective reference numerals 802 to 804indicate sensory evaluation prediction waveforms of N response in asection 1 to a section 3. Respective reference numerals 805 to 807indicate sensory evaluation prediction waveforms of straightness in thesection 1 to the section 3.

In the example illustrated in FIG. 12 , the evaluation target for thesensory index N response is in the section 1 and the section 3 that canbe determined as a slalom run from the steering operation, and thesection 2 in which only straight traveling is performed is excluded fromthe evaluation target. In the straightness as the sensory index, thesection 1 and the section 3 are excluded from the evaluation target, andthe section 2 is the evaluation target. The evaluation circuit targetsthe sensory index for steering stability and targets an event withfluctuation, and therefore it is predicted that a constant fixed valueis not output. However, since assumed learning has not been performed inthe steering operation excluded from the evaluation target, it ispredicted that the waveform is saturated to the upper limit value as ina reference numeral 805 and a reference numeral 807, or the waveformlargely varies as in the reference numeral 803.

In other words, reliability of the sensory evaluation prediction valueduring traveling in the steering operation excluded from the evaluationtarget is considered to be low. On the other hand, since the assumedlearning has been performed in the steering operation as the evaluationtarget, a value in a certain range is considered to be output, and thewaveforms, such as the reference numeral 802, the reference numeral 804,and a reference numeral 806, are expected. Accordingly, the evaluationindex determination unit 106 determines whether each of the evaluationcircuits 130 is the evaluation target or not using the steeringinformation and eliminates the sensory evaluation prediction value inthe steering operation excluded from the evaluation target.

FIG. 13 includes drawings illustrating examples of visualizing the indexvalues output by the aggregation unit constituting the sensoryevaluation prediction system. FIG. 13(a) is an example that visualizesthe sensory evaluation prediction values for the five types of sensoryindices and the graph type is a radar chart. Note that the sensory indexto be visualized is selectable by a setting value (not illustrated)stored in the register 104. The setting values (not illustrated) are acollection of one-bit registers corresponding to the sensory indices,and is, for example, constituted by a N response selection register, ayaw response selection register, a grip feeling selection register, aroll feeling selection register, and a straightness selection register.When “1” is set as each of the register values, the sensory index isdisplayed, and when “0” is set as each of the register values, thesensory index is not displayed. Accordingly, the case of FIG. 13(a)shows the case where all of the register values of the five types ofdisplay selection registers are set as “1.”

Additionally, aggregating methods of the sensory evaluation predictionvalues are differentiated based on the setting values of the aggregationmode setting 114. For example, when the register value of theaggregation mode setting 114 is “1,” the sensory evaluation predictionvalue is the average value of the sensory evaluation prediction valueduring traveling in the steering operation as the evaluation target, andwhen the register value of the aggregation mode setting 114 is “0,” thesensory evaluation prediction value is the instantaneous value duringtraveling in the steering operation as the evaluation target. This ismerely an example, the register value that is possibly taken may beexpanded, and treated as a moving average value, and further a registerhaving a two-bit width or more to set a window width during calculationof the moving average may be set.

FIG. 13(b) is an example of visualizing one type of sensory index, thegrip feeling, and the type of the graph is a bar chart. In this case,only the grip feeling selection register has the register value of “1,”and the registers other than that is equivalent to the case of being setto non-display. Since the setting specifications of the aggregation modesetting 114 are similar to the case of FIG. 13(a) described above, thedescription will be omitted. Note that FIG. 13 displays the sensoryindex value with 6.0 points as the reference. Although “6.0” is notessential, it is important to visualize the sensory evaluationprediction value in the display specification for comparison with thereference point.

FIG. 14 is a timing chart illustrating an exemplary relationship betweenan operation of the evaluation circuit constituting the sensoryevaluation prediction system according to the first embodiment and atraveling road surface.

Reference numeral 1001 indicates a steering operation type detectiontiming waveform, a reference numeral 1002 indicates an operation timingof evaluation circuit for N response, a reference numeral 1003 indicatesan operation timing of evaluation circuit for yaw response, a referencenumeral 1004 indicates an operation timing of evaluation circuit forgrip feeling, a reference numeral 1005 indicates an operation timing ofevaluation circuit for roll feeling, and a reference numeral 1006indicates an operation timing of evaluation circuit for straightness.

First, the steering operation 801 transitions from the section 1 to thesection 3 via the section 2. The steering operation information isacquired, and the evaluation index determination unit 106 detects thetype of steering operation. In FIG. 14 , the detection is performed at atiming when the pulse waveform of the reference numeral 1001 is High(1). At the section 1, the evaluation index determination unit 106determines that the sensory evaluation is performed for the sensoryindices, the N response, the yaw response, and the roll feeling, andoutputs evaluation index selection signals 1002, 1004, and 1006 thatbecome High (1) during traveling on the road surface as the evaluationtarget. Meanwhile, the evaluation index determination unit 106 outputsevaluation index selection signals 1003 and 1005 that become Low (0) forthe grip feeling and the straightness excluded from the evaluationtarget.

The switching timing of the evaluation index selection signal is atiming of the steering operation type detection. Note that FIG. 14illustrates the evaluation index selection signals 1002 to 1006 assumingthe case where the steering operation type is switched like the steeringoperation 801, but this is merely an example. There are a variety ofways of thinking of sensory indices, and the evaluation index selectionsignals are created according to the way of thinking.

Note that in this embodiment, for simplifying the contents for ease ofunderstanding, the two-dimensional image formed of the two types of thephysical quantity P1 and the physical quantity P2 has been mainlydescribed. However, as long as good and bad of the sensory index forsteering stability can be determined, it is not limited to thetwo-dimensional image, and may be a three-dimensional data space usingthe three types or more of physical quantities or may be a data spacemore than three dimensions. Especially, on the premise of a hierarchicalneural network, an information volume is not necessarily reduced to aninformation volume that can be grasped by a person.

According to the above-described first embodiment, the following effectscan be obtained.

(1) The sensory evaluation prediction system 101 includes the input unit115, the selection unit 105, the correlation creation unit 120, and theevaluation circuit 130. The input unit 115 reads the output from thebehavior sensor that measures two or more types of pieces of the timeseries information regarding the moving body. The selection unit 105selects two or more types of the physical quantities from the outputfrom the behavior sensor read by the input unit 115. The correlationcreation unit 120 creates the information showing the correlation intime series between the two or more types of the physical quantitiesselected by the selection unit 105. The evaluation circuit 130calculates the evaluation value of the sensor index based on theinformation showing the correlation in time series. Therefore, with thesensory evaluation prediction system 101, since the correlation of aplurality of the physical quantities is evaluated, a variation of anoperation depending on a driver is less likely to be affected.

(2) The evaluation circuit 130 is configured to calculate the pluralityof sensory indices. The sensory evaluation prediction system 101includes the evaluation index determination unit 106 and the register104. The evaluation index determination unit 106 determines the sensoryindex for the evaluation target based on the steering operation of themoving body. The register 104 is a storage unit that stores the dataspecification setting 113 that makes the two or more physical quantitiescorrespond to the corresponding sensory index determined by theevaluation index determination unit 106. The selection unit 105 refersto the data specification setting 113 and determines the two or morephysical quantities based on the determination by the evaluation indexdetermination unit 106. This allows evaluating the steering stabilityfor steering operation by the driver using the appropriate evaluationindices.

(3) When the selection unit 105 selects the two physical quantities, thecorrelation creation unit 120 plots the correlation in time seriesbetween the two physical quantities on the two-dimensional plane andoutputs the plot as the raster image information. Therefore, thecorrelation in time series between the two physical quantities can beexpressed in the simplified manner, and is robust against datavariation. Outputting the plot as vector image information is alsoconsidered. However, considering the use to the input to input layer inthe hierarchical neural network, the vector image information has lowrobustness and obtaining the stable output is difficult with the vectorimage information. Therefore, as in this embodiment, the method thatuses the raster image information, in other words, the value of eachpixel for the input to the input layer is excellent.

(4) When the selection unit 105 selects the three physical quantities,the correlation creation unit 120 plots the correlation in time seriesbetween the three physical quantities on the three-dimensional space,and outputs the plot as voxel information. Therefore, the correlation intime series between the three physical quantities can be expressed inthe simplified manner, and is robust against data variation.

(5) The evaluation circuit 130 includes a plurality of small evaluationcircuits corresponding to the respective plurality of sensory indices,namely, the first evaluation circuit 131 to the fifth evaluation circuit135. The control unit 103 stops any of the evaluation circuits 130 thatdoes not calculate the sensory index based on the selection by theevaluation index determination unit 106. Therefore, power consumptioncan be reduced. This is especially effective when the sensory evaluationprediction system 101 is mounted on the vehicle.

(6) The data specification setting 113 has the combinations of thephysical quantities different depending on the sensory indices.Therefore, the combination of the physical quantities optimal to each ofthe sensory indices can be used.

(7) The sensory evaluation prediction system 101 is mounted on themoving body. The input unit 115 reads the output from the behaviorsensor mounted on the moving body. The sensory evaluation predictionsystem 101 includes the aggregation unit 108 that aggregates thecalculation results by the evaluation circuits 130. The aggregation unit108 is configured to switch between an instantaneous evaluation mode anda comprehensive evaluation mode. The instantaneous evaluation modeoutputs the instantaneous value or the moving average value of thecalculation result by the evaluation circuit 130. The comprehensiveevaluation mode outputs the average value of the calculation results bythe evaluation circuit for a predetermined period.

Modification 1

In the first embodiment described above, the evaluation indexdetermination unit 106 determines the evaluation index based on thesteering information using the method of pattern matching. However, thesteering information and the evaluation index may be associated by aninference based on learning using the hierarchical neural network. Inthis hierarchical neural network, for example, the steering informationin time series separated in units of certain periods is an input, and anelement corresponding to each of the sensory indices is an element ofthe output layer. In a learning phase, the weight parameter is learntsuch that the element corresponding to the sensory index answered by theexpert driver becomes “1” and the others become “0.”

According to this modification, the following effects can be obtained inaddition to the effects of the first embodiment described above.

(8) The evaluation index determination unit 106 determines therelationship between the information of the steering operation and thesensory index based on the learning. While above-described patternmatching needs to determine whether it is the evaluation target orexcluded from the evaluation target in advance, application of thehierarchical neural network has the following advantage. That is,teacher data can be acquired during a sensory evaluation test in whichthe expert driver drives, and the evaluation index selection suitablefor the actual way of thinking can be achieved.

Modification 2

The sensory evaluation prediction system 101 need not include the testresult storage unit 102 when mounted on the vehicle. In the case, theoutput from the sensor group mounted on the vehicle is input to theinput unit 115.

Modification 3

The sensory evaluation prediction system 101 may evaluate only onesensory index. In the case, the sensory evaluation prediction system 101need not include the evaluation index determination unit 106.

Second Embodiment

With reference to FIG. 15 and FIG. 16 , the second embodiment of asensory evaluation prediction system will be described. In the followingdescription, the same reference numerals are given to the componentssame as those of the first embodiment, and the differences will bemainly described. Points not specifically described are the same asthose of the first embodiment. This embodiment mainly differs from thefirst embodiment in that one evaluation circuit is used in common forthe plurality of sensory indices.

FIG. 15 is a block diagram of a sensory evaluation prediction system101A in the second embodiment. The second embodiment differs from thefirst embodiment in that an evaluation unit 107A includes only acorrelation creation unit 126 and an evaluation circuit 136.

The correlation creation unit 126 communalizes the first correlationcreation unit 121 to the fifth correlation creation unit 125 describedin FIG. 1 in the first embodiment. The correlation creation unit 126acquires information of the evaluation index evaluated by the evaluationcircuit 136 via the control unit 103 and creates correlation informationin time series by targeting the plurality of physical quantitiescorresponding to the evaluation index. The correlation creation unit 126outputs the created correlation information to the evaluation circuit136. Note that while only one correlation creation unit 126 isillustrated in the example of FIG. 15 , a plurality of the correlationcreation units 126 may be present inside the evaluation unit 107A. Atleast one evaluation circuit 136 and one correlation creation unit 126are used in common for two types or more of the sensory indices, anygiven number of the correlation creation units 126 can be disposedinside the evaluation unit 107A.

The evaluation circuit 136 commonalizes the first evaluation circuit 131to the fifth evaluation circuit 135 for each of the sensory indicesdescribed in FIG. 1 in the first embodiment for a plurality of thesensory indices. That is, the correlation creation unit 126 and theevaluation circuit 136 are used in common for the above-describedrespective sensory indices, for example, the five types of sensoryindices, which are the N response, the yaw response, the grip feeling,the roll feeling, and the straightness. Note that in the example of FIG.15 , only one evaluation circuit 136 is illustrated, but a plurality ofthe evaluation circuits 136 may be present inside the evaluation unit107A. At least one evaluation circuit 136 is disposed in common for twotypes or more of the sensory indices, any given number of the evaluationcircuits 136 can be used inside the evaluation unit 107A. Further, thenumber of the correlation creation units 126 need not be the same asthat of the evaluation circuits 136.

FIG. 16 is a flowchart depicting a flow of processes of the sensoryevaluation prediction system according to the second embodiment of thepresent invention. Compared with the flowchart in FIG. 11 described inthe first embodiment, the flowchart in FIG. 16 differs in that StepS1201 is provided instead of Step S707. Note that, except for theprocessing step different from that of the first embodiment, thefollowing will omit the description unless otherwise necessary.

At Step S1201, the control unit 103 reads the weight parameter of theevaluation circuit corresponding to the sensory index selected as theevaluation index at Step S706 from the weight parameter storage unit109. Then, the read weight parameter is set to the evaluation circuit136. Thus, the evaluation circuit 136 is adjusted according to theevaluation index in the evaluation unit 107A.

At Step S711, the evaluation circuit 136 adjusted according to theevaluation index at Step S1201 calculates the evaluation value for theevaluation index based on the data for evaluation input from theselection unit 105 at Step S710.

The second embodiment of the present invention described above providesthe following effects in addition to the effects similar to the firstembodiment.

(9) The evaluation circuit 130 includes a small evaluation circuitcorresponding to the plurality of sensory indices in common, namely, theevaluation circuit 136. The evaluation circuit 136 is adjusted accordingto the evaluation index selected by the evaluation index determinationunit 106, and the evaluation value is calculated using the adjustedevaluation circuit 136. Specifically, the evaluation circuit 136 isestablished using the neural network in which a plurality of elementsare hierarchically coupled, and the weight parameter for each of theelements is adjusted according to the evaluation index. This allowsachieving a reduction in circuit scale.

Third Embodiment

With reference to FIG. 17 and FIG. 18 , the third embodiment of asensory evaluation prediction system will be described. In the followingdescription, the same reference numerals are given to the componentssame as those of the first embodiment, and the differences will bemainly described. Points not specifically described are the same asthose of the second embodiment. This embodiment mainly differs from thesecond embodiment in that the physical quantity used to estimate thesensory index is determined by learning and the data specificationsetting is created.

FIG. 17 is a block diagram of a sensory evaluation prediction system101B in the third embodiment. In this embodiment, the register 104further stores a search mode 1041 and a learning determination thresholdvalue 1042. However, the register 104 need not store the dataspecification setting 113 at the time of starting a process describedlater, and the data specification setting 113 is created by the processdescribed later. In this embodiment, at the time of starting the processdescribed later, the weight parameter storage unit 109 need not storedata, and the weight parameter storage unit 109 stores the data by theprocess described later.

When “1” is set to the search mode 1041, the sensory evaluationprediction system 101B transitions to the search mode and creates thedata specification setting 113. When “0” is set to the search mode 1041,the sensory evaluation prediction system 101B transitions to anon-search mode, and performs the operations described in the firstembodiment using the preliminarily created data specification setting113 or the data specification setting 113 read from the outside.

In this embodiment, a learning unit 107B is disposed instead of theevaluation unit 107A. The learning unit 107B further includes a learningfunction, which will be described next, in addition to the function ofthe evaluation unit 107A in the second embodiment. The learning unit107B performs the operation similar to that of the second embodiment inthe non-search mode and achieves the learning function in the searchmode.

The learning unit 107B in the search mode searches for the combinationof the physical quantities used for estimation of the sensory index asfollows. First, the learning unit 107B selects any arbitrary combinationof the physical quantities and creates the first correlationinformation. Next, the learning unit 107B learns a relationship betweenthe first correlation information and the sensory index value forsteering stability acquired from the expert driver in the hierarchicalneural network. Then, when an output error of the hierarchical neuralnetwork is smaller than the learning determination threshold value 1042,that is, when a difference with the teacher data decreases by a constantamount or more, the learning unit 107B determines that the learning ispossible. The learning unit 107B records the combination of the physicalquantities used for the first correlation information to the dataspecification setting 113, and stores the parameter obtained through thelearning in the weight parameter storage unit 109.

On the other hand, the learning in the hierarchical neural network isattempted, when the output error of the hierarchical neural network isthe learning determination threshold value 1042 or more, that is, thedifference with the teacher data does not decrease by the constantamount or more, it is determined that the learning is impossible. Inthis case, the combination of the different physical quantities isselected, the second correlation information is created, and learning ofthe relationship between the second correlation information and thesensory index value for steering stability acquired from the expertdriver is attempted in the hierarchical neural network. Thus, thecombination of the physical quantities is searched until it isdetermined that the learning is possible.

Note that various methods can be employed as the search method for thecombination of the physical quantities used to create the correlationinformation. For example, two or more types of a plurality of physicalquantities may be randomly selected from a plurality of physicalquantities, priority orders are given to the physical quantities bysensation of the expert driver, and the combination may be searched inhigh priority order. Furthermore, like reinforcement learning as one ofArtificial Intelligence (AI), the search may be performed in trial anderror through evaluation.

FIG. 18 is a flowchart depicting processes of a learning function of asensory evaluation prediction system 101C in the third embodiment.First, at Step S1402, the control unit 103 determines whether the searchmode 1041 is set to on by the operation by the vehicle occupant in ownvehicle or the like, that is, whether the learning mode is set to bevalid. When “1” is set to the search mode 1041, the control unit 103determines that the operation command of learning has been performed,and advances the process to Step S1403, and when “0” is set to thesearch mode 1041, the process returns to Step S1402.

Subsequently, the control unit 103 acquires the steering operationinformation (Step S1403) and analyzes the steering operation in timeseries (Step S1404). At Step S1405, the control unit 103 determines thesensory index corresponding to the steering operation using theevaluation index determination unit 106.

At Step S1406, the control unit 103 determines the combination of thephysical quantities corresponding to the sensory index determined atStep S1405. As described above, the combination of the physicalquantities is, for example, randomly determined. At the subsequent StepS1407, the control unit 103 reads the information of the physicalquantities determined at Step S1406 from the learning target 1021 in thetest result storage unit 102 and the sensory index by the expert driver.At the subsequent Step S1408, the learning unit 107B learns using thephysical quantities read at Step S1407 and the sensory index by theexpert driver.

At Step S1409, the control unit 103 determines whether the output erroris smaller than the learning determination threshold value 1042. Whenthe control unit 103 determines that the output error is the learningdetermination threshold value 1042 or more, the control unit 103 returnsthe process to Step S1406, employs the different combination of thephysical quantities, and advances the process to at and after StepS1407. When the control unit 103 determines that the output error issmaller than the learning determination threshold value 1042, thecontrol unit 103 records the combination of the physical quantitiesdetermined at Step S1406 to the data specification setting 113, andstores the parameter obtained through the learning at Step S1408 in theweight parameter storage unit 109.

According to the third embodiment described above, the following effectscan be obtained.

(10) The test result storage unit 102 stores the learning target 1021 asthe combination of the output from the behavior sensor and theevaluation value of the sensory index. The learning unit 107B uses thelearning target 1021 to learn the combination of the two or more typesof the physical quantities used for the calculation of the evaluationvalue and included in the output from the behavior sensor. The learningunit 107B attempts the learning using the combination of the pluralityof arbitrarily selected physical quantities. When the output error bythe learning is smaller than the learning determination threshold value1042, the learning unit 107B makes the sensory index correspond to theplurality of arbitrarily selected physical quantities and records it tothe data specification setting 113. Therefore, while the learning of thehierarchical neural network in the evaluation circuit 130 is performed,the appropriate combination of the physical quantities can be searched.Further, this search helps solving the relationship between the steeringoperation. The vehicle behavior at the time, and the sensory evaluationby the vehicle occupant.

Fourth Embodiment

The fourth embodiment of the present invention will be described belowwith reference to FIG. 19 . In this embodiment, an example ofmanufacturing a suspension device using the sensory evaluationprediction system will be described.

FIG. 19 is a block diagram illustrating a functional configuration of asensory evaluation prediction system according to the fourth embodimentof the present invention. Compared with the sensory evaluationprediction system 101 in FIG. 1 described in the first embodiment, thesensory evaluation prediction system 101C illustrated in FIG. 19 differsin that the sensory evaluation prediction system 101C is mounted on amoving body, further includes a transmission/reception unit 901, and isconnected to a computer center 150 and an evaluation value collectioncenter 1502 via a network. Further, the sensory evaluation predictionsystem 101C includes a sensor group 900 instead of the test resultstorage unit 102.

The transmission/reception unit 901 is connected to the computer center150 via the network, such as the Internet, receives learnt data, such asthe weight parameter, transmitted from the computer center 150, andoutputs it to the control unit 103. The learnt data includes, forexample, evaluation index determination data used to select the sensoryindex (the evaluation index) as the evaluation target for each type ofroad surfaces among the plurality of types of the sensory indices by theevaluation index determination unit 106, the weight parameter stored inthe weight parameter storage unit 109, and the data specificationsetting 113.

The sensor group 900 is, for example, an acceleration sensor, a gyrosensor, a vehicle speed sensor, a camera, and a laser range finder. Theoutput from the sensor group 900 is input to the input unit 115.

The evaluation value collection center 1502 collects the evaluationvalues for the respective sensory indices calculated and aggregated bythe sensory evaluation prediction system 101C by own vehicle travelingvarious roads and provides them to a designer 1503. Note that theevaluation value collection center 1502 may be connected to a pluralityof the sensory evaluation prediction systems 101C mounted on therespective different vehicles, and may be able to collect the evaluationvalues from the respective sensory evaluation prediction systems 101C.The designer 1503 to whom the evaluation values are provided from theevaluation value collection center 1502 designs a suspension device 1505with reference to the evaluation values, and provides design informationto a manufacturing process 1504. The manufacturing process 1504 to whomthe design information is provided manufactures the suspension device1505 using the design information. This allows manufacturing thesuspension device 1505 based on the evaluation values output from thesensory evaluation prediction system 101C.

Note that the example in which the suspension device 1505 ismanufactured using the evaluation values output from the sensoryevaluation prediction system 101C similarly to the sensory evaluationprediction system 101 described in the first embodiment has beendescribed above. However, similarly to the sensory evaluation predictionsystems 101A and 101B described in the respective second and thirdembodiments, the sensory evaluation prediction system 101C may beconfigured, and the suspension device 1505 may be manufactured using theevaluation values output from the sensory evaluation prediction system101C.

According to the fourth embodiment of the present invention describedabove, the following effects can be obtained.

(11) The suspension device 1505 is manufactured based on the evaluationvalue output from the sensory evaluation prediction system 101C.Accordingly, the evaluation value for each of the sensory indicesacquired for various roads can be easily reflected to manufacture thesuspension device 1505. Therefore, the suspension device having highperformance in improvement in a ride quality can be provided.

Modification 1 of Fourth Embodiment

Instead of the sensory evaluation prediction system 101C including thesensor group 900, the sensor group 900 may be mounted on a vehicle onwhich the sensory evaluation prediction system 101C is mounted. Insteadof the sensory evaluation prediction system 101C including the displayunit 111, the display unit 111 may be mounted on a vehicle on which thesensory evaluation prediction system 101C is mounted.

Fifth Embodiment

The fifth embodiment of the present invention will be described belowwith reference to FIG. 20 . In this embodiment, an example of a controlsystem configured to adjust a damping force of a suspension device usingthe sensory evaluation prediction system will be described.

FIG. 20 is a block diagram illustrating a functional configuration of asuspension control system according to the fifth embodiment of thepresent invention. The suspension control system illustrated in FIG. 20includes a sensory evaluation prediction system 101D and a suspensiondamping force variable mechanism 1702. Note that the configuration andthe operation of the sensory evaluation prediction system 101D aresimilar to those of the sensory evaluation prediction system 101 in FIG.1 described in the first embodiment.

The suspension damping force variable mechanism 1702 adjusts the dampingforce of a suspension device (not illustrated) mounted on own vehiclebased on the evaluation value for each of the sensory indices outputfrom the sensory evaluation prediction system 101D. For example, acontrol command value or a control parameter according to the evaluationvalue is set to the suspension device configured to adjust the dampingforce according to the control command value or the control parameterinput from the outside. This allows reflecting the sensory evaluationresult obtained by the sensory evaluation prediction system 101D andadjusting the suspension device.

Generally, the suspension device changes a damping force propertyaccording to an oil leakage and a secular change of a mechanicalproperty, and provides an influence on a ride quality of an automobilein some cases. Therefore, in the suspension control system of thisembodiment, when a change in the evaluation value is detected under asimilar traveling environment in an automobile on which the sensoryevaluation prediction system 101D is mounted, the suspension dampingforce variable mechanism 1702 adjusts the damping force of thesuspension device so as to cancel the change. Thus, even when a failureor deterioration occurs in the suspension device, the property of thesuspension can be corrected, thereby allowing lengthening the used timeof the suspension device. Furthermore, the suspension property may bechanged according to the type of the road surface on which own vehicletravels. This allows always providing an optimal ride quality regardlessof the type of the road surface.

Note that the example in which the damping force of the suspensiondevice is adjusted by the suspension damping force variable mechanism1702 using the evaluation value output from the sensory evaluationprediction system 101D similarly to the sensory evaluation predictionsystem 101 described in the first embodiment has been described above.However, the sensory evaluation prediction system 101D may be configuredsimilarly to the sensory evaluation prediction systems 101A and 101Bdescribed in the respective second and third embodiments and the dampingforce of the suspension device may be adjusted using the evaluationvalue output from the sensory evaluation prediction system 101D.

According to the fifth embodiment of the present invention describedabove, the following effects can be obtained.

(12) The suspension control system includes the sensory evaluationprediction system 101D and the suspension damping force variablemechanism 1702. The suspension damping force variable mechanism 1702adjusts the damping force of the suspension device mounted on ownvehicle based on the evaluation value output from the sensory evaluationprediction system 101D. This allows lengthening the used time of thesuspension device and allows providing the suspension device configuredto provide the optimal ride quality regardless of the type of the roadsurface.

The configuration of the function block in each of the embodiments andthe modifications described above are merely an example. Some functionalconfigurations described as different function blocks may be integrated,or the configuration expressed by one function block diagram may bedivided into two or more functions. Additionally, another function blockmay include a part of the function that each of the function blocks has.

Each of the embodiments and the modifications described above may becombined. While the various embodiments and modifications have beendescribed above, the present invention is not limited to these contents.Other aspects considered within the scope of the technical idea of thepresent invention are also included in the scope of the presentinvention.

This application is based upon, and claims the benefit of priority from,corresponding Japanese Patent Application No. 2020-11277 filed in theJapan Patent Office on Jan. 28, 2020, the entire contents of which areincorporated herein by reference.

LIST OF REFERENCE SIGNS

101, 101A, 101B, 101C, 101D sensory evaluation prediction system

102 test result storage unit

103 control unit

104 register

105 selection unit

106 evaluation index determination unit

107, 107A evaluation unit

107B learning unit

108 aggregation unit

109 weight parameter storage unit

110 aggregation result storage unit

111 display unit

112 sensory index setting

113 data specification setting

114 aggregation mode setting

115 input unit

120 correlation creation unit

130 evaluation circuit

201 vehicle

401 evaluation circuit

801 steering operation

1021 learning target

1022 evaluation target

1041 search mode

1042 learning determination threshold value

1504 manufacturing process

1505 suspension device

1702 suspension damping force variable mechanism

1. A sensory evaluation prediction system comprising: an input unit thatreads an output from a behavior sensor that measures two or more typesof pieces of time series information regarding a moving body; aselection unit that selects two or more types of physical quantitiesfrom the output from the behavior sensor read by the input unit; acorrelation creation unit that creates information showing a correlationin time series between the two or more types of the physical quantitiesselected by the selection unit; and an evaluation circuit thatcalculates an evaluation value of a sensory index based on theinformation showing the correlation in time series.
 2. The sensoryevaluation prediction system according to claim 1, wherein theevaluation circuit is configured to calculate a plurality of the sensoryindices, the sensory evaluation prediction system further includes: anevaluation index determination unit that determines the sensory indexfor an evaluation target based on a steering operation of the movingbody; and a storage unit that stores a data specification setting thatmakes the two or more physical quantities correspond to thecorresponding sensory index determined by the evaluation indexdetermination unit, and the selection unit refers to the dataspecification setting and determines the two or more physical quantitiesbased on the determination by the evaluation index determination unit.3. The sensory evaluation prediction system according to claim 1,wherein when the selection unit selects two physical quantities, thecorrelation creation unit plots a correlation in time series between thetwo physical quantities on a two-dimensional plane and outputs the plotas raster image information.
 4. The sensory evaluation prediction systemaccording to claim 1, wherein when the selection unit selects threephysical quantities, the correlation creation unit plots a correlationin time series between the three physical quantities on athree-dimensional space, and outputs the plot as voxel information. 5.The sensory evaluation prediction system according to claim 2, whereinthe evaluation circuit includes a plurality of small evaluation circuitscorresponding to the respective plurality of sensory indices, and thesensory evaluation prediction system further includes a control unitthat stops the small evaluation circuit that does not calculate thesensory index based on a selection by the evaluation index determinationunit.
 6. The sensory evaluation prediction system according to claim 1,wherein the evaluation circuit includes a small evaluation circuitcorresponding to the plurality of sensory indices in common, the smallevaluation circuit is adjusted according to the evaluation index, andthe evaluation value is calculated using the adjusted small evaluationcircuit.
 7. The sensory evaluation prediction system according to claim2, wherein, the evaluation index determination unit determines arelationship between information of the steering operation and thesensory index based on learning.
 8. The sensory evaluation predictionsystem according to claim 2, wherein the data specification setting hascombinations of the physical quantities different depending on thesensory indices.
 9. The sensory evaluation prediction system accordingto claim 2, wherein the storage unit further stores a learning target asa combination of the output from the behavior sensor and the evaluationvalue of the sensory index, the sensory evaluation prediction systemfurther includes a learning unit that uses the learning target to learnthe combination of the two or more types of the physical quantities usedfor the calculation of the evaluation value and included in the outputfrom the behavior sensor, and the learning unit attempts the learningusing the combination of the plurality of arbitrarily selected physicalquantities, and when an output error by the learning is smaller than apredetermined learning determination threshold value, the learning unitmakes the sensory index correspond to the plurality of arbitrarilyselected physical quantities and records the sensory index and theplurality of arbitrarily selected physical quantities to the dataspecification setting.
 10. The sensory evaluation prediction systemaccording to claim 1, wherein the sensory evaluation prediction systemis mounted on the moving body, the input unit reads the output from thebehavior sensor mounted on the moving body, the sensory evaluationprediction system further includes an aggregation unit that aggregatescalculation results by the evaluation circuit, and the aggregation unitis configured to switch between an instantaneous evaluation mode and acomprehensive evaluation mode, the instantaneous evaluation mode outputsan instantaneous value or a moving average value of the calculationresult by the evaluation circuit, and the comprehensive evaluation modeoutputs an average value of the calculation result by the evaluationcircuit for a predetermined period.
 11. A suspension device manufacturedbased on the evaluation value output from the sensory evaluationprediction system according to claim
 1. 12. A suspension control systemcomprising: the sensory evaluation prediction system according to claim1; and a suspension damping force variable mechanism that adjusts adamping force of a suspension device mounted on the moving body based onthe evaluation value output from the sensory evaluation predictionsystem.